velocyto
Mikaela Rosen
6/29/2021
Pre-processing of the Data to create a Loom File
In order to calculate RNA Velocity using Velocyto, you first need to produce a Loom file. This file can be made in two different ways, described here and briefly summarized below.
Before using either tool on Minerva, the anaconda environment must be created as follows.
# Create the anaconda environment
ml anaconda3/2020.11; CONDA_BASE=$(conda info --base); source $CONDA_BASE/etc/profile.d/conda.sh; ml purge
# Run this line only once for initial setup
mamba create -n velocyto_env velocyto.py bustools kallisto kb-python
# Activate anaconda environment to use
conda activate velocyto_env
# Deactivate environment when finished
conda deactivate1) Using Velocyto.py
This is a Python implementation of Velocyto. The entire Velocyto analysis pipeline can be run using this Python tool. Importantly, the pre-processing (aka the creation of the loom file) can only be run in the Python and not in the R. This method requires, at minimum, a Binary Alignment Map (BAM) file for your data and a Gene Transfer Format (GTF) gene annotation file. See Velocyto's Python documentation for more details.
Here is an example of the command to create the loom file from BAM files made using any sequencing technique and GTF file downloaded from CellRanger.
# Run velocyto.py using its generic 'run' command with the bare minimum inputs
velocyto run -o out -v /hpc/users/rosenm36/ad-omics/mynd-ad/single_cell_res/velocity_res/sc_bams_myndad/possorted_genome_bam_P1.bam /hpc/users/rosenm36/ad-omics/mynd-ad/single_cell_res/velocity_res/gtf_annotation/refdata-gex-GRCh38-2020-A/genes/genes.gtfNote: It is cited that typical use of this command takes ~3h and this time varies based on sequencing depth and CPU power. On Minerva, the process was not finished by the 3h mark. Because of this, we used the alternative method described next.
2) Using Kallisto
In Kallisto, use the kb function. First, either build or download (as shown here) a reference file. To make reference files you'll need FASTA and GTF files. Second, generate a counts table which can be output as a loom file. See Kallisto|Bustools documentation for additional details.
# Download the index files of Kallisto
kb ref -d linnarsson -i index.idx -g t2g.txt -c1 spliced_t2c.txt -c2 unspliced_t2c.txt
# Generate the Loom file
# (example submits to a computing node)
## x specifies single-cell technology
## --lamanno specifies we want to calculate RNA velocity
echo "kb count -t 20 --loom -i index.idx -g t2g.txt -x 10xv3 -o mic_sc \
-c1 spliced_t2c.txt -c2 unspliced_t2c.txt --workflow lamanno --filter bustools \
/sc/arion/projects/ad-omics/sc_mic_rawdata/30-410769069/00_fastq/MG-22-HIPP/MG-22-HIPP_S1_L001_R1_001.fastq.gz \
/sc/arion/projects/ad-omics/sc_mic_rawdata/30-410769069/00_fastq/MG-22-HIPP/MG-22-HIPP_S1_L001_R2_001.fastq.gz \
/sc/arion/projects/ad-omics/sc_mic_rawdata/30-410769069/00_fastq/MG-22-MFG/MG-22-MFG_S1_L001_R1_001.fastq.gz \
/sc/arion/projects/ad-omics/sc_mic_rawdata/30-410769069/00_fastq/MG-22-MFG/MG-22-MFG_S1_L001_R2_001.fastq.gz" | bsub -n 20 -R "rusage[mem=2000]" -R "span[hosts=1]" -W 12:00 -oo kb.out -eo kb.err -P acc_ad-omics -q express -J kb Understanding the Loom File
The Linnarson lab developed loom, an HDF5-based data structure, to easily store single cell datasets and metadata. The Satija lab created the loomR package in response to allow for analysis of loom files using the Seurat pipeline. Their tutorial is very helpful to learn about the structure of a loom file and how to work with it. Similarly, here I explore loom files using our data.
A loom object is a container for six sub-objects: one dataset five groups. The general sub-objects include the following:
- Matrix - with two dimensions of n genes and m cells
- Layers - alternative representations of the data with the same dimensions as the original data
- row_attrs and col_attrs - row and column names
- row_graphs and col_graphs - row and column graphs
Again, see the tutorial for more information about official loom objects.
# remotes::install_github("lambdamoses/BUStoolsR")
library(BUSpaRse)
library(Seurat)## Attaching SeuratObjectlibrary(SeuratWrappers)
library(velocyto.R)## Loading required package: Matrixlibrary(ggplot2)
# Create path to loom files created in pre-processing
d <- "kallisto_res/mic_sc/counts_filtered"
# Read in the loom file
ldat <- read_velocity_output(spliced_dir = d, spliced_name = "spliced", unspliced_dir = d, unspliced_name = "unspliced")
# View summary information about our data
summary(ldat)## Length Class Mode
## spliced 537618437 dgCMatrix S4
## unspliced 537618437 dgCMatrix S4str(ldat)## List of 2
## $ spliced :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. ..@ i : int [1:8905876] 47 67 72 77 118 140 145 163 199 203 ...
## .. ..@ p : int [1:9212] 0 1907 3191 4627 5529 7143 8302 8554 9736 10783 ...
## .. ..@ Dim : int [1:2] 58367 9211
## .. ..@ Dimnames:List of 2
## .. .. ..$ : chr [1:58367] "ENSG00000277400.1" "ENSG00000274847.1" "ENSG00000276256.1" "ENSG00000278198.1" ...
## .. .. ..$ : chr [1:9211] "AAACCCAAGGTGCCTC" "AAACCCACAGACTGCC" "AAACCCAGTTAAGTCC" "AAACCCATCGCAATTG" ...
## .. ..@ x : num [1:8905876] 1 1 1 1 7 1 1 2 1 2 ...
## .. ..@ factors : list()
## $ unspliced:Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. ..@ i : int [1:11666203] 7 8 48 60 72 144 148 157 163 165 ...
## .. ..@ p : int [1:9212] 0 2453 3953 5868 7008 9019 10704 11155 13023 14066 ...
## .. ..@ Dim : int [1:2] 58367 9211
## .. ..@ Dimnames:List of 2
## .. .. ..$ : chr [1:58367] "ENSG00000277400.1" "ENSG00000274847.1" "ENSG00000276256.1" "ENSG00000278198.1" ...
## .. .. ..$ : chr [1:9211] "AAACCCAAGGTGCCTC" "AAACCCACAGACTGCC" "AAACCCAGTTAAGTCC" "AAACCCATCGCAATTG" ...
## .. ..@ x : num [1:11666203] 1 1 2 2 1 1 1 2 1 2 ...
## .. ..@ factors : list()As you can see, in our data we have two dgCMatrix objects as our 'loom' object. One of these objects holds the spliced and the other the unspliced data. This type of object is specifically designed to hold sparse numeric matrices in the compressed, sparse, column-oriented format. It follows a slightly different format, although the general components are similar to those of loom files - crucially they both hold a matrix and details about rows/columns.
Within each dgCMatrix object are particular slots:
- i - holds the row index of the non-zero elements
- ex: the kth index of slot i holds the row index for the kth non-zero element in the object
- p - holds the cumulative number of non-zero elements as we move from one column to the next column, left to right
- its length is equal to [ncols(matrix) + 1] and the first value is always 0
- ex: the jth index of slot p holds the the number of non-zero elements in columns 0 to j-1 (inclusive)
- note: since p is a cumulative sum, we can use diff() to get the number of non-zero entries in each column
- x - holds the non-zero elements in the data sorted column-wise (top to bottom, left to right)
- ex: all non-zero entries in column 1 are followed by those in column 2
- Dim and Dimnames - denotes the number and names (respectively) of the rows and columns
- factors - a list of factorizations of the matrix
- typically empty and is updated automatically whenever a matrix factorization is computed
To learn more about dgCMatrix objects and the slots within them check out this blog and this sparse matrix tutorial.
# View the `matrix` dataset with the double subset [[ operator or using $ sign
## our matrix are called spliced or unspliced
#ldat[["spliced"]] not run
#ldat$spliced
# Access specific parts of the data using indexing
ldat[["spliced"]][1:5, 1:5]## 5 x 5 sparse Matrix of class "dgCMatrix"
## AAACCCAAGGTGCCTC AAACCCACAGACTGCC AAACCCAGTTAAGTCC
## ENSG00000277400.1 . . .
## ENSG00000274847.1 . . .
## ENSG00000276256.1 . . .
## ENSG00000278198.1 . . .
## ENSG00000273496.1 . . .
## AAACCCATCGCAATTG AAACCCATCGCTAAAC
## ENSG00000277400.1 . .
## ENSG00000274847.1 . .
## ENSG00000276256.1 . .
## ENSG00000278198.1 . .
## ENSG00000273496.1 . .Calculating RNA Velocity using Velocyto and the Seurat Pipeline
Our analysis followed the steps outlined in a Satija Lab vignette which illustrates how to estimate RNA velocity using Seurat objects.
#why was this done?
emat <- ldat$spliced
nmat <- ldat$unspliced
# Use Seurat functions to complete the analysis
bm <- as.Seurat(x = ldat) #convert loom data to seurat## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')class(bm)## [1] "Seurat"
## attr(,"package")
## [1] "SeuratObject"summary(bm)## Length Class Mode
## 1 Seurat S4str(bm)## Formal class 'Seurat' [package "SeuratObject"] with 13 slots
## ..@ assays :List of 2
## .. ..$ spliced :Formal class 'Assay' [package "SeuratObject"] with 8 slots
## .. .. .. ..@ counts :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. .. .. .. .. ..@ i : int [1:8905876] 47 67 72 77 118 140 145 163 199 203 ...
## .. .. .. .. .. ..@ p : int [1:9212] 0 1907 3191 4627 5529 7143 8302 8554 9736 10783 ...
## .. .. .. .. .. ..@ Dim : int [1:2] 58367 9211
## .. .. .. .. .. ..@ Dimnames:List of 2
## .. .. .. .. .. .. ..$ : chr [1:58367] "ENSG00000277400.1" "ENSG00000274847.1" "ENSG00000276256.1" "ENSG00000278198.1" ...
## .. .. .. .. .. .. ..$ : chr [1:9211] "AAACCCAAGGTGCCTC" "AAACCCACAGACTGCC" "AAACCCAGTTAAGTCC" "AAACCCATCGCAATTG" ...
## .. .. .. .. .. ..@ x : num [1:8905876] 1 1 1 1 7 1 1 2 1 2 ...
## .. .. .. .. .. ..@ factors : list()
## .. .. .. ..@ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. .. .. .. .. ..@ i : int [1:8905876] 47 67 72 77 118 140 145 163 199 203 ...
## .. .. .. .. .. ..@ p : int [1:9212] 0 1907 3191 4627 5529 7143 8302 8554 9736 10783 ...
## .. .. .. .. .. ..@ Dim : int [1:2] 58367 9211
## .. .. .. .. .. ..@ Dimnames:List of 2
## .. .. .. .. .. .. ..$ : chr [1:58367] "ENSG00000277400.1" "ENSG00000274847.1" "ENSG00000276256.1" "ENSG00000278198.1" ...
## .. .. .. .. .. .. ..$ : chr [1:9211] "AAACCCAAGGTGCCTC" "AAACCCACAGACTGCC" "AAACCCAGTTAAGTCC" "AAACCCATCGCAATTG" ...
## .. .. .. .. .. ..@ x : num [1:8905876] 1 1 1 1 7 1 1 2 1 2 ...
## .. .. .. .. .. ..@ factors : list()
## .. .. .. ..@ scale.data : num[0 , 0 ]
## .. .. .. ..@ key : chr "spliced_"
## .. .. .. ..@ assay.orig : NULL
## .. .. .. ..@ var.features : logi(0)
## .. .. .. ..@ meta.features:'data.frame': 58367 obs. of 0 variables
## .. .. .. ..@ misc : list()
## .. ..$ unspliced:Formal class 'Assay' [package "SeuratObject"] with 8 slots
## .. .. .. ..@ counts :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. .. .. .. .. ..@ i : int [1:11666203] 7 8 48 60 72 144 148 157 163 165 ...
## .. .. .. .. .. ..@ p : int [1:9212] 0 2453 3953 5868 7008 9019 10704 11155 13023 14066 ...
## .. .. .. .. .. ..@ Dim : int [1:2] 58367 9211
## .. .. .. .. .. ..@ Dimnames:List of 2
## .. .. .. .. .. .. ..$ : chr [1:58367] "ENSG00000277400.1" "ENSG00000274847.1" "ENSG00000276256.1" "ENSG00000278198.1" ...
## .. .. .. .. .. .. ..$ : chr [1:9211] "AAACCCAAGGTGCCTC" "AAACCCACAGACTGCC" "AAACCCAGTTAAGTCC" "AAACCCATCGCAATTG" ...
## .. .. .. .. .. ..@ x : num [1:11666203] 1 1 2 2 1 1 1 2 1 2 ...
## .. .. .. .. .. ..@ factors : list()
## .. .. .. ..@ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. .. .. .. .. ..@ i : int [1:11666203] 7 8 48 60 72 144 148 157 163 165 ...
## .. .. .. .. .. ..@ p : int [1:9212] 0 2453 3953 5868 7008 9019 10704 11155 13023 14066 ...
## .. .. .. .. .. ..@ Dim : int [1:2] 58367 9211
## .. .. .. .. .. ..@ Dimnames:List of 2
## .. .. .. .. .. .. ..$ : chr [1:58367] "ENSG00000277400.1" "ENSG00000274847.1" "ENSG00000276256.1" "ENSG00000278198.1" ...
## .. .. .. .. .. .. ..$ : chr [1:9211] "AAACCCAAGGTGCCTC" "AAACCCACAGACTGCC" "AAACCCAGTTAAGTCC" "AAACCCATCGCAATTG" ...
## .. .. .. .. .. ..@ x : num [1:11666203] 1 1 2 2 1 1 1 2 1 2 ...
## .. .. .. .. .. ..@ factors : list()
## .. .. .. ..@ scale.data : num[0 , 0 ]
## .. .. .. ..@ key : chr "unspliced_"
## .. .. .. ..@ assay.orig : NULL
## .. .. .. ..@ var.features : logi(0)
## .. .. .. ..@ meta.features:'data.frame': 58367 obs. of 0 variables
## .. .. .. ..@ misc : list()
## ..@ meta.data :'data.frame': 9211 obs. of 5 variables:
## .. ..$ orig.ident : Factor w/ 1 level "SeuratProject": 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ nCount_spliced : num [1:9211] 4816 2181 2597 1506 2942 ...
## .. ..$ nFeature_spliced : int [1:9211] 1907 1284 1436 902 1614 1159 252 1182 1047 590 ...
## .. ..$ nCount_unspliced : num [1:9211] 5148 2476 3850 1768 3818 ...
## .. ..$ nFeature_unspliced: int [1:9211] 2453 1500 1915 1140 2011 1685 451 1868 1043 604 ...
## ..@ active.assay: chr "spliced"
## ..@ active.ident: Factor w/ 1 level "SeuratProject": 1 1 1 1 1 1 1 1 1 1 ...
## .. ..- attr(*, "names")= chr [1:9211] "AAACCCAAGGTGCCTC" "AAACCCACAGACTGCC" "AAACCCAGTTAAGTCC" "AAACCCATCGCAATTG" ...
## ..@ graphs : list()
## ..@ neighbors : list()
## ..@ reductions : list()
## ..@ images : list()
## ..@ project.name: chr "SeuratProject"
## ..@ misc : list()
## ..@ version :Classes 'package_version', 'numeric_version' hidden list of 1
## .. ..$ : int [1:3] 4 0 0
## ..@ commands : list()
## ..@ tools : list()# Any additional steps of analysis get tagged onto your Seurat object
bm <- SCTransform(object = bm, assay = "spliced")## Calculating cell attributes from input UMI matrix: log_umi## Variance stabilizing transformation of count matrix of size 29246 by 9211## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 5000 cells##
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|
|================== | 25%## Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|
|=================================== | 50%## Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|
|==================================================== | 75%## Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|======================================================================| 100%## Found 37 outliers - those will be ignored in fitting/regularization step## Second step: Get residuals using fitted parameters for 29246 genes##
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|======================================================================| 100%## Calculating gene attributes## Wall clock passed: Time difference of 2.496695 mins## Determine variable features## Place corrected count matrix in counts slot## Centering data matrix## Set default assay to SCTbm <- RunPCA(object = bm, verbose = FALSE) #stored under reductions
bm <- FindNeighbors(object = bm, dims = 1:20) #stored under graphs## Computing nearest neighbor graph## Computing SNNbm <- FindClusters(object = bm)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 9211
## Number of edges: 333608
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8257
## Number of communities: 18
## Elapsed time: 1 secondsbm <- RunUMAP(object = bm, dims = 1:20)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 15:39:25 UMAP embedding parameters a = 0.9922 b = 1.112## 15:39:25 Read 9211 rows and found 20 numeric columns## 15:39:25 Using Annoy for neighbor search, n_neighbors = 30## 15:39:25 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 15:39:26 Writing NN index file to temp file /tmp/RtmpXTmfee/file37b2f4bc4bd2a
## 15:39:26 Searching Annoy index using 1 thread, search_k = 3000
## 15:39:29 Annoy recall = 100%
## 15:39:30 Commencing smooth kNN distance calibration using 1 thread
## 15:39:32 Initializing from normalized Laplacian + noise
## 15:39:33 Commencing optimization for 500 epochs, with 406094 positive edges
## 15:40:04 Optimization finishedbm <- RunVelocity(object = bm, deltaT = 1, kCells = 25, fit.quantile = 0.02) #note to self: go back and check parameters for this## Filtering genes in the spliced matrix
## Filtering genes in the unspliced matrix
## Calculating embedding distance matrixident.colors <- (scales::hue_pal())(n = length(x = levels(x = bm)))
names(x = ident.colors) <- levels(x = bm)
cell.colors <- ident.colors[Idents(object = bm)]
names(x = cell.colors) <- colnames(x = bm)
#pdf("microglia_velocyto_plot.pdf", width = 9, height = 9)
show.velocity.on.embedding.cor(emb = Embeddings(object = bm, reduction = "umap"), vel = Tool(object = bm,
slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5),
cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1,
do.par = FALSE, cell.border.alpha = 0.1)
## delta projections ... sqrt knn ... transition probs ... done
## calculating arrows ... done
## grid estimates ... grid.sd= 0.4175762 min.arrow.size= 0.008351525 max.grid.arrow.length= 0.0443544 done#dev.off()Feature Plots
In order to assess the clustering and velocity results, we observe the expression levels of previously identified features. These help us hypothesize which clusters are particular cell types. In this case, I visualized genes that were more highly expressed in homeostatic microglia and those more highly expressed in monocytes (according to prior analysis by previous rotation student Emily Kozik).
The data is labeled with EnsemblID gene names rather than gene symbols. If you change those labels before making your Seurat object, you should not run into this issue. However, EnsemblID gene names and gene symbols often do not match at a 1:1 rate, so re-labeling them will likely lose information. The other option, which is used here, is to analyze using the EnsemblID names and then change the labels on output plots and tables to be gene symbols.
#Feature Plots
library(ggpubr)
#function to make multipannel feature plot
make_featurePlot <- function(ensembles, names) {
#create list object to hold plots
plot_list = vector(mode = "list", length = length(ensembles))
#initialize index
index = 1
for (gene in ensembles) {
#make feature plot with labels
featurePlot_func <- FeaturePlot(bm, features = gene, reduction = 'umap',
max.cutoff = 2, cols = c("lightgrey", "darkblue"),
ncol = 2)
#this line is required to re-label the data with gene names instead of their ensembleIDs
featurePlot_func = featurePlot_func + labs(title = names[index])
#add plot to list
plot_list[[index]] = featurePlot_func
index = index + 1
}
#merge them using ggarrange
arranged = ggarrange(plotlist = plot_list)
#return
return(arranged)
}
#Homeostatic Gene Set
title = "Homeostatic Gene Set"
ensemb = c("ENSG00000168329.13", "ENSG00000171659.15", "ENSG00000169313.9", "ENSG00000181631.7")
names = c("CX3CR1", "GPR34", "P2RY12", "P2RY13")
figure_homeo = make_featurePlot(ensemb, names)
#annotate the figure
annotate_figure(figure_homeo, top = text_grob(title,
color = "black",
face = "bold",
size = 16)
)
#other (ie monocyte markers)
title = "Monocyte Gene Set"
ensemb = c("ENSG00000204472.13", "ENSG00000173372.17", "ENSG00000011600.11")
names = c("AIF1","C1QA","TYROBP")
figure_other = make_featurePlot(ensemb, names)
figure_other
#annotate the figure
annotate_figure(figure_other, top = text_grob(title,
color = "black",
face = "bold",
size = 16)
)
#save.image(file = paste("/hpc/users/rosenm36/ad-omics/mynd-ad/single_cell_res/velocity_res/microglia_velocyto.RData", sep = ""))
#load(paste("/hpc/users/rosenm36/ad-omics/mynd-ad/single_cell_res/velocity_res/microglia_velocyto.RData", sep = ""))The clustering results found in our initial analysis was not too similar to what was found in previous analyses. Adjusting the analysis so that it more closely resembles the previous analysis may lead to more comparable results.
Exploring Parameters
Several steps were changed in order to make the previous and current analyses more similar.
# Use Seurat functions to complete the analysis
bm_2 <- as.Seurat(x = ldat) #convert loom data to seurat## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')class(bm_2)## [1] "Seurat"
## attr(,"package")
## [1] "SeuratObject"summary(bm_2)## Length Class Mode
## 1 Seurat S4str(bm_2)## Formal class 'Seurat' [package "SeuratObject"] with 13 slots
## ..@ assays :List of 2
## .. ..$ spliced :Formal class 'Assay' [package "SeuratObject"] with 8 slots
## .. .. .. ..@ counts :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. .. .. .. .. ..@ i : int [1:8905876] 47 67 72 77 118 140 145 163 199 203 ...
## .. .. .. .. .. ..@ p : int [1:9212] 0 1907 3191 4627 5529 7143 8302 8554 9736 10783 ...
## .. .. .. .. .. ..@ Dim : int [1:2] 58367 9211
## .. .. .. .. .. ..@ Dimnames:List of 2
## .. .. .. .. .. .. ..$ : chr [1:58367] "ENSG00000277400.1" "ENSG00000274847.1" "ENSG00000276256.1" "ENSG00000278198.1" ...
## .. .. .. .. .. .. ..$ : chr [1:9211] "AAACCCAAGGTGCCTC" "AAACCCACAGACTGCC" "AAACCCAGTTAAGTCC" "AAACCCATCGCAATTG" ...
## .. .. .. .. .. ..@ x : num [1:8905876] 1 1 1 1 7 1 1 2 1 2 ...
## .. .. .. .. .. ..@ factors : list()
## .. .. .. ..@ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. .. .. .. .. ..@ i : int [1:8905876] 47 67 72 77 118 140 145 163 199 203 ...
## .. .. .. .. .. ..@ p : int [1:9212] 0 1907 3191 4627 5529 7143 8302 8554 9736 10783 ...
## .. .. .. .. .. ..@ Dim : int [1:2] 58367 9211
## .. .. .. .. .. ..@ Dimnames:List of 2
## .. .. .. .. .. .. ..$ : chr [1:58367] "ENSG00000277400.1" "ENSG00000274847.1" "ENSG00000276256.1" "ENSG00000278198.1" ...
## .. .. .. .. .. .. ..$ : chr [1:9211] "AAACCCAAGGTGCCTC" "AAACCCACAGACTGCC" "AAACCCAGTTAAGTCC" "AAACCCATCGCAATTG" ...
## .. .. .. .. .. ..@ x : num [1:8905876] 1 1 1 1 7 1 1 2 1 2 ...
## .. .. .. .. .. ..@ factors : list()
## .. .. .. ..@ scale.data : num[0 , 0 ]
## .. .. .. ..@ key : chr "spliced_"
## .. .. .. ..@ assay.orig : NULL
## .. .. .. ..@ var.features : logi(0)
## .. .. .. ..@ meta.features:'data.frame': 58367 obs. of 0 variables
## .. .. .. ..@ misc : list()
## .. ..$ unspliced:Formal class 'Assay' [package "SeuratObject"] with 8 slots
## .. .. .. ..@ counts :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. .. .. .. .. ..@ i : int [1:11666203] 7 8 48 60 72 144 148 157 163 165 ...
## .. .. .. .. .. ..@ p : int [1:9212] 0 2453 3953 5868 7008 9019 10704 11155 13023 14066 ...
## .. .. .. .. .. ..@ Dim : int [1:2] 58367 9211
## .. .. .. .. .. ..@ Dimnames:List of 2
## .. .. .. .. .. .. ..$ : chr [1:58367] "ENSG00000277400.1" "ENSG00000274847.1" "ENSG00000276256.1" "ENSG00000278198.1" ...
## .. .. .. .. .. .. ..$ : chr [1:9211] "AAACCCAAGGTGCCTC" "AAACCCACAGACTGCC" "AAACCCAGTTAAGTCC" "AAACCCATCGCAATTG" ...
## .. .. .. .. .. ..@ x : num [1:11666203] 1 1 2 2 1 1 1 2 1 2 ...
## .. .. .. .. .. ..@ factors : list()
## .. .. .. ..@ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## .. .. .. .. .. ..@ i : int [1:11666203] 7 8 48 60 72 144 148 157 163 165 ...
## .. .. .. .. .. ..@ p : int [1:9212] 0 2453 3953 5868 7008 9019 10704 11155 13023 14066 ...
## .. .. .. .. .. ..@ Dim : int [1:2] 58367 9211
## .. .. .. .. .. ..@ Dimnames:List of 2
## .. .. .. .. .. .. ..$ : chr [1:58367] "ENSG00000277400.1" "ENSG00000274847.1" "ENSG00000276256.1" "ENSG00000278198.1" ...
## .. .. .. .. .. .. ..$ : chr [1:9211] "AAACCCAAGGTGCCTC" "AAACCCACAGACTGCC" "AAACCCAGTTAAGTCC" "AAACCCATCGCAATTG" ...
## .. .. .. .. .. ..@ x : num [1:11666203] 1 1 2 2 1 1 1 2 1 2 ...
## .. .. .. .. .. ..@ factors : list()
## .. .. .. ..@ scale.data : num[0 , 0 ]
## .. .. .. ..@ key : chr "unspliced_"
## .. .. .. ..@ assay.orig : NULL
## .. .. .. ..@ var.features : logi(0)
## .. .. .. ..@ meta.features:'data.frame': 58367 obs. of 0 variables
## .. .. .. ..@ misc : list()
## ..@ meta.data :'data.frame': 9211 obs. of 5 variables:
## .. ..$ orig.ident : Factor w/ 1 level "SeuratProject": 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ nCount_spliced : num [1:9211] 4816 2181 2597 1506 2942 ...
## .. ..$ nFeature_spliced : int [1:9211] 1907 1284 1436 902 1614 1159 252 1182 1047 590 ...
## .. ..$ nCount_unspliced : num [1:9211] 5148 2476 3850 1768 3818 ...
## .. ..$ nFeature_unspliced: int [1:9211] 2453 1500 1915 1140 2011 1685 451 1868 1043 604 ...
## ..@ active.assay: chr "spliced"
## ..@ active.ident: Factor w/ 1 level "SeuratProject": 1 1 1 1 1 1 1 1 1 1 ...
## .. ..- attr(*, "names")= chr [1:9211] "AAACCCAAGGTGCCTC" "AAACCCACAGACTGCC" "AAACCCAGTTAAGTCC" "AAACCCATCGCAATTG" ...
## ..@ graphs : list()
## ..@ neighbors : list()
## ..@ reductions : list()
## ..@ images : list()
## ..@ project.name: chr "SeuratProject"
## ..@ misc : list()
## ..@ version :Classes 'package_version', 'numeric_version' hidden list of 1
## .. ..$ : int [1:3] 4 0 0
## ..@ commands : list()
## ..@ tools : list()Normalization and Scaling
The normalization technique was changed from SCTransform to LogNormalization. This means using log normalization followed by scaling the data based on variable features. This normalizes the feature expression measurements for each cell by the total expression, multiplies this by a scale factor, and log-transforms the result. The scaling then shifts the expression of each gene so that the mean expression across cells is 0 and scales the expression of each gene so that the variance across cells is 1, giving equal weight to genes in downstream analyses so that highly-expressed genes do not dominate.
bm_2 <- NormalizeData(bm_2, normalization.method = "LogNormalize", scale.factor = 10000)
bm_2 <- FindVariableFeatures(bm_2, selection.method = "vst", nfeatures = 2000) #need to do this to run PCA
all.genes_bm_2 <- rownames(bm_2)
bm_2 <- ScaleData(bm_2, features = all.genes_bm_2) #,vars.to.regress = c("percent.mt","nCount_RNA") do I have access to this info? ## Centering and scaling data matrixPCA
The PCA parameters were changed to explicitly use variable features. The default behavior of PCA runs using the variable features for the Assay. Using the SCTransform method, I did not have to explicitly specify the variable features. When using LogNormalization and scaling, I was prompted to specify variable features for this command.
bm_2 <- RunPCA(object = bm_2, features = VariableFeatures(bm_2), verbose = FALSE)FindNeighbors and FindClusters
FindNeighbors was changed to run on default parameters.
The resolution was changed to 0.1 for FindClusters, which parallels previous analysis and should results in a smaller number of communities. A resolution parameter value above 1.0 obtains a larger number of communities and below obtains smaller number of communities or clusters.
bm_2 <- FindNeighbors(object = bm_2) #default is dim = 1:10## Computing nearest neighbor graph## Computing SNNbm_2 <- FindClusters(object = bm_2, resolution = 0.1) #default is resolution = 0.8## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 9211
## Number of edges: 298752
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9281
## Number of communities: 5
## Elapsed time: 1 secondsUMAP
An elbow plot was created to evaluate how many PCs are needed to capture the majority of variation in the data. The elbow is vaguely defined as where the plot angles to flatten horizontally, suggesting a drop-off in standard deviation and our threshold. Obviously, this is a very qualitative measure and other methods can be used to qualitatively create a threshold. Based on our plot, it seemed most variation is accounted for by PCs 1 to 13. Therefore, RunUMAP was changed to use the first 13 PCs.
#check for # of PCs to use in UMAP
ElbowPlot(bm_2)
bm_2 <- RunUMAP(object = bm_2, dims = 1:13) #default is 1:5## 16:20:33 UMAP embedding parameters a = 0.9922 b = 1.112## 16:20:33 Read 9211 rows and found 13 numeric columns## 16:20:33 Using Annoy for neighbor search, n_neighbors = 30## 16:20:33 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 16:20:35 Writing NN index file to temp file /tmp/RtmpXTmfee/file37b2f380ceca9
## 16:20:35 Searching Annoy index using 1 thread, search_k = 3000
## 16:20:39 Annoy recall = 100%
## 16:20:41 Commencing smooth kNN distance calibration using 1 thread
## 16:20:44 Initializing from normalized Laplacian + noise
## 16:20:44 Commencing optimization for 500 epochs, with 400224 positive edges
## 16:21:15 Optimization finished#check what the UMAP looks like
DimPlot(bm_2, reduction = 'umap')
Velocity
The RunVelocity command requires an object with information on the spliced and unspliced reads. You can also specify which reduction method the command should use, with the default being PCA.
Excluding the main parameters, there are additional parameters the function can take in. Most notably:
- deltaT - the amount of time to project the cell forward; default = 1
- kCells - number of k nearest neighbors (NN) to use in slope calculation smoothing; default = 10
- fit.quantile - perform gamma fit on a [fit.quantile]% of top/bottom quantiles of expression magnitudes
- recommended to do if can afford to do kNN smoothing
- ex: 0.02 means top/bottom 2% expression quantiles
bm_2 <- RunVelocity(object = bm_2, deltaT = 1, kCells = 25, fit.quantile = 0.02) #note to self: go back and check parameters for this## Filtering genes in the spliced matrix## Filtering genes in the unspliced matrix## Calculating embedding distance matrixident.colors <- (scales::hue_pal())(n = length(x = levels(x = bm_2)))
names(x = ident.colors) <- levels(x = bm_2)
cell.colors <- ident.colors[Idents(object = bm_2)]
names(x = cell.colors) <- colnames(x = bm_2)
#pdf("microglia_velocyto_newParams_plot.pdf", width = 9, height = 9)
show.velocity.on.embedding.cor(emb = Embeddings(object = bm_2, reduction = "umap"), vel = Tool(object = bm_2,
slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5),
cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1,
do.par = FALSE, cell.border.alpha = 0.1)
## delta projections ... sqrt knn ... transition probs ... done
## calculating arrows ... done
## grid estimates ... grid.sd= 0.3864034 min.arrow.size= 0.007728068 max.grid.arrow.length= 0.0443544 done#dev.off()